CN111665808A - Production scheduling plan optimization method based on genetic algorithm - Google Patents
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- G05B19/41865—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM] characterised by job scheduling, process planning, material flow
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- G05B2219/00—Program-control systems
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Abstract
The invention relates to a production scheduling plan optimization method based on a genetic algorithm, which optimizes order production line distribution and production sequence of spinning enterprises with relatively formed equipment in a production mode taking orders as driving force, and improves production efficiency by adjusting processing sequences and flows of different types of textiles. The method comprises the steps of establishing a detailed project requirement of production plan scheduling optimization, establishing a model, designing a genetic algorithm aiming at the proposed model, and realizing a specific algorithm so as to generate an optimal production plan scheduling result through the algorithm.
Description
Technical Field
The invention relates to an optimization method, in particular to a production scheduling plan optimization method based on a genetic algorithm.
Background
The genetic algorithm is a calculation model for simulating the natural selection of Darwin evolutionary theory and the biological evolution process of genetic mechanism, and is a method for searching an optimal solution by simulating the natural evolution process. Genetic algorithms begin with a population representing a potential solution set to the problem, and a population is composed of a certain number of individuals that are genetically encoded. After the initial generation population is generated, according to the principle of survival and the principle of excellence and disadvantage of fittest, generation-by-generation evolution generates better and better approximate solutions, in each generation, individuals are selected according to the fitness of the individuals in the problem, and combination crossing and variation are performed by means of genetic operators of natural genetics to generate a population representing a new solution set. This process will result in populations that evolve like nature, with offspring populations more adaptive to the environment than the previous generation, and with the optimal individuals in the last generation population corresponding as the optimal solution for the problem approximation.
The specific flow of the genetic algorithm is shown in figure 1 in detail, firstly, a chromosome (code) representing a problem solution is determined aiming at a problem, the formation of an initial population is realized, whether a termination condition is met or not is judged according to the evaluation of a fitness function, and if so, an optimal solution is output; and if not, selecting the population according to the fitness value, returning to the evaluation according to the fitness function after cross mutation treatment, and performing next judgment until an optimal solution is obtained.
Disclosure of Invention
The invention provides a production scheduling plan optimization method based on a genetic algorithm, which solves the problem that the order production line distribution and the production sequence are optimized for spinning enterprises with relatively shaped equipment under the production mode taking orders as driving force, and the production efficiency is improved by adjusting the processing sequence and the flow of different types of textiles. The technical scheme is as follows:
a production scheduling plan optimization method based on a genetic algorithm comprises the following steps:
s1: analyzing the detailed project requirements of the production plan scheduling optimization, determining the content of the order and defining the batch according to the order;
s2: converting the batch information into model parameters, and determining the total amount, completion date and production process of the batch;
s3: constructing a model adopting a genetic algorithm, and determining related constraint conditions;
s4: processing the model by using a genetic algorithm, and constructing a target function, initializing variables, intersecting, assigning values and mutating until an optimal solution, namely an optimal production schedule, is generated;
s5: and displaying the optimal production plan scheduling result generated by the algorithm.
Further, in step S1, when the textile industry receives different orders in a short period of time, the textile industry splits the batches of different textiles in the order, and the batches in different orders are produced in a unified manner.
Further, in step S2, the total amount of the batches is the sum of the required amounts of each batch.
Further, in step S3, the constraint conditions include process constraint, machine constraint, and completion time constraint:
(1) process restraint: only after the last process task is processed and finished, the next process can be processed on the same production line:
Cij(k+1)-Cijk-Pij(k+1)≥0(Xijk+Xij(k+1)＜＝1)
(2) and (3) machine constraint: the same production line can process the next batch only after the previous batch is processed:
C(i+1)jk-Cijk-P(i+1)jk≥0(X(i+1)jk+Xijk＜＝1)
(4) and (4) completing time constraint:
Cij(k+1)＝max{Cijk，STij(k+1)}+Pi2(k+1)
wherein, the ith batch is on the jth production line, and the time C _ (ij (k +1)) for completing the (k +1) th process satisfies the following constraint:
Cij(k+1)＝STijk+Pij(k+1)
wherein, CiFor the completion time of the ith lot, CijkIndicates the end time, P, of the kth process step when the ith lot is produced on the jth production lineijkIndicates the processing time of the kth process in the production of the ith lot on the jth production line, STijkIndicating the start time of the kth process when the ith batch is produced on the jth production line.
The machine constraints include the following:
(6) a factory machine number configuration table;
(7) a production line configuration table;
(8) the manufacturing rate of each procedure-equipment operation rate-equipment operation time table;
(9) a machine production parameter configuration table;
(10) dry-wet weight ratio of the product;
(6) each process switches the product-required schedule.
Further, in step S3, the objective function of the model is as follows:
wherein F is the minimum maximum completion time, m is the number of batches, i means the ith batch, CiIs the completion time of the ith lot.
Further, in step S4, the specific genetic algorithm is implemented as follows:
s41: determining input and output variables and an encoding mode, wherein the input variables are as follows: order information; output variables are: an optimal production scheduling scheme; and (3) an encoding mode: splitting the order into a plurality of batches, and fully arranging the batch numbers;
s42: constructing a fitness function GeneticAlgorithm, namely sequentially calculating the production time of production for individuals formed in batches according to an algorithm;
s43: randomly generating an initial population farm and calculating a fitness function, wherein each individual is a production scheduling scheme in the population farm;
s44: sequencing the individuals according to a fitness function, and reserving the optimal Parent solution, namely reserving the best Parent production scheduling scheme;
s45: performing cross processing, namely selecting two parent schemes to be crossed from a plurality of randomly generated production scheduling schemes, wherein the specific cross operation is to exchange the batch production sequence in the two scheduling schemes and then generate two new child schemes;
s46: performing mutation treatment, namely generating a new production scheduling scheme by a random method to avoid the occurrence of local convergence;
s47: steps S44 through S46 are repeated until the fitness function converges, resulting in an optimal solution.
The production scheduling plan optimization method based on the genetic algorithm realizes the improvement of production efficiency, saves time, reduces cost and can effectively distribute and manage by optimizing the existing formed spinning process, thereby realizing the good development of production.
Drawings
FIG. 1 is a flow chart of a genetic algorithm;
FIG. 2 is an overall flow diagram of the present invention;
FIG. 3 is a diagram illustrating the scheduling result of the optimal production plan generated by the present invention in practical application.
Detailed Description
Under the production mode taking orders as driving force, the method is suitable for optimizing the production line distribution aspect of spinning enterprises with relatively shaped equipment, and improves the production efficiency by adjusting the production line distribution of different orders.
According to the production scheduling plan optimization method based on the genetic algorithm, the specific application environment and related parameters are changed, as shown in fig. 2, the method comprises the following steps:
s1: analyzing the detailed project requirements of the production plan scheduling optimization;
on the premise that the production task of a textile enterprise is based on an order, the detailed project requirements refer to: the content of the order is determined, batch information is defined according to the order, and process influencing factors of the workpiece are defined according to parameters of production equipment in a production workshop, so that the process from the order to model building can be smoothly realized.
Batch: when textile enterprises receive different orders in a short time, different textile in the orders are split into batches, and the batches in the different orders are produced in a unified mode. The method defines a textile in an order as a "batch". For example: the enterprise receives three orders in a short period of time, with order one containing A, B, C three types of textiles, order two containing A, C two textiles, and order three containing B, C two. Then, the production plan contains a total of 7 batches.
In summary, when a textile enterprise receives different orders in a short time, the processing requirements of the orders are split into batches, and the batches are produced respectively.
S2: converting model parameters;
when the model is established, a mathematical language is needed, and according to the description of the detailed project requirements, the specific mathematical language is described as follows:
among the orders received by the enterprise, there are i kinds of yarn fabrics (spun yarns) which are produced in i batches, the total amount of each batch being NiThe total amount is based on the final output of the spinning frame.
The method comprises the following steps of sequentially carrying out seven working procedures on different batches of i textiles, namely blowing-carding-drawing, pre-drawing, sliver-doubling and rolling, combing, final drawing, roving and spun yarn which are sequentially produced.
In an operation embodiment, for example, if a new order exists, a variety, a required weight and a finishing date are input according to the content of the order, for example, two types of textiles exist in the new order, the first type is the variety A, the weight is 15 tons, and the finishing date is 12 months and 10 days in XX year; the second is variety B, weighing 10 tons, ending at 12 months and 20 days XX years.
The two scrims were produced in two batches, the total amount of which was 25 tons. Both batches of textiles are processed through seven sequential processes.
S3: constructing a model adopting a genetic algorithm;
a model using a genetic algorithm is constructed, and the scheduling aims to minimize the maximum completion time.
Process influencing factors need to be considered at this time: the influencing factors comprise the manufacturing rate of each process, the running rate of equipment, the running time of the equipment, a machine production parameter configuration table and the dry and wet weight of a produced product. And then the number of the factory equipment and various parameter information need to be input so as to determine constraint conditions, wherein the constraint conditions comprise process constraint, machine constraint, completion time constraint and the like.
Therefore, in operation, the number of plant devices and various parameter information are also required to be recorded and stored in a relevant table, and the information includes the following contents:
(1) a factory machine number configuration table;
(2) a production line configuration table;
(3) the manufacturing rate of each procedure-equipment operation rate-equipment operation time table;
(4) a machine production parameter configuration table;
(5) the dry and wet weight of the product is g-m;
(6) each process switches the product-required schedule.
Therefore, the constraint conditions are determined through data interaction, and the optimal scheme can be generated by utilizing an algorithm subsequently.
Based on the above analysis, the following mathematical optimization model was constructed:
an objective function:
wherein the content of the first and second substances,
f is the minimum maximum completion time, m is the number of batches, i is the ith batch, CiIs the completion time of the ith lot.
Constraint conditions are as follows:
(1) process restraint: the next working procedure processing can be carried out only after the previous working procedure task processing is finished on the same production line.
Cij(k+1)-Cijk-Pij(k+1)≥0(Xijk+Xij(k+1)＜＝1)
(2) And (3) machine constraint: in the same production line, only after the previous batch is processed, the next batch can be processed.
C(i+1)jk-Cijk-P(i+1)jk≥0(X(i+1)jk+Xijk＜＝1)
(5) And (4) completing time constraint:
Cij(k+1)＝max{Cijk，STij(k+1)}+Pi2(k+1)
wherein, the ith batch is on the jth production line, and the time C _ (ij (k +1)) for completing the (k +1) th process satisfies the following constraint:
Cij(k+1)＝STijk+Pij(k+1)
wherein, CiFor the completion time of the ith lot, CijkIndicates the end time, P, of the kth process step when the ith lot is produced on the jth production lineijkIndicates the processing time of the kth process in the production of the ith lot on the jth production line, STijkIndicating the start time of the kth process when the ith batch is produced on the jth production line.
(4) At the start of the process, all the textile material is ready;
(5) each batch was processed sequentially in the order of 7 passes, and only once.
When the method is used for the first time aiming at an order, the quantity of factory equipment and various parameter information need to be input, and the more the relevant information is, the more the information is, the more clear the process constraint, the machine constraint and the completion time constraint can be judged. In the subsequent order processing, the previously entered information can be directly used, and if the information needs to be adjusted, subsequent operations are performed after the adjustment.
S4: realizing a specific genetic algorithm;
the specific genetic algorithm is realized by constructing an objective function, initializing variables, intersecting, assigning values, compiling and the like until an optimal solution, namely an optimal production schedule, is generated.
The method comprises the following specific steps:
the first step is as follows: determining input and output variables and an encoding mode. Input variables are: order information, output variables: an optimal production scheduling scheme, a coding mode: splitting the order into a plurality of batches, and fully arranging the batch numbers;
the second step is that: constructing a fitness function GeneticAlgorithm, namely sequentially calculating the production time for production according to an algorithm individual;
the third step: an initial population farm is randomly generated and a fitness function is calculated. In the group farm, each individual is a production scheduling scheme;
the fourth step: and sequencing the individuals according to a fitness function, and reserving the optimal Parent solutions, namely reserving the best Parent production scheduling schemes.
The fifth step: and (4) crossing. Two parents are selected from the population, the random selection of crossover patterns and crossover points is performed to generate two new offspring chromosomes, and the new and old chromosomes are combined into a new population. That is, in a plurality of randomly generated production scheduling schemes, two parent schemes are selected to be crossed, and the specific operation of crossing is to exchange the production sequence of batches in the two scheduling schemes and then generate two new child schemes;
and a sixth step: and (5) carrying out mutation. Genes to be mutated are randomly selected in the population, and new chromosomes are generated to avoid the situation of local convergence. Namely, a new production scheduling scheme is generated by a random method;
the seventh step: and repeating the fourth step to the sixth step until the fitness function is converged to generate an optimal solution.
And finally, storing the optimal scheme into a relevant table for data storage.
S5: and displaying the optimal production plan scheduling result generated by the algorithm.
As shown in FIG. 3, the selected production lines for each lot are described in detail, as well as the start time and end time in each production line.
The production scheduling plan optimization method based on the genetic algorithm realizes the improvement of production efficiency, saves time, reduces cost and can effectively distribute and manage by optimizing the existing formed spinning process, thereby realizing the good development of production.
Claims (7)
1. A production scheduling plan optimization method based on a genetic algorithm comprises the following steps:
s1: analyzing the detailed project requirements of the production plan scheduling optimization, determining the content of the order and defining the batch according to the order;
s2: converting the batch information into model parameters, and determining the total amount, completion date and production process of the batch;
s3: constructing a model adopting a genetic algorithm, and determining related constraint conditions;
s4: processing the model by using a genetic algorithm, and constructing a target function, initializing variables, intersecting, assigning values and mutating until an optimal solution, namely an optimal production schedule, is generated;
s5: and displaying the optimal production plan scheduling result generated by the algorithm.
2. The genetic algorithm-based production scheduling plan optimization method of claim 1, wherein: in step S1, when the textile enterprise receives different orders in a short period, the textile enterprise splits the batches of different textiles in the order, and performs unified production on the batches in different orders.
3. The genetic algorithm-based production scheduling plan optimization method of claim 1, wherein: in step S2, the total amount of the lot is the sum of the required amounts for each lot.
4. The genetic algorithm-based production scheduling plan optimization method of claim 1, wherein: in step S3, the constraint conditions include process constraint, machine constraint, and completion time constraint:
(1) process restraint: only after the last process task is processed and finished, the next process can be processed on the same production line:
Cij(k+1)-Cijk-Pij(k+1)≥0(Xijk+Xij(k+1)＜＝1)
(2) and (3) machine constraint: the same production line can process the next batch only after the previous batch is processed:
C(i+1)jk-Cijk-P(i+1)jk≥0(X(i+1)jk+Xijk＜＝1)
(3) and (4) completing time constraint:
Cij(k+1)＝max{Cijk，STij(k+1)}+Pi2(k+1)
wherein, the ith batch is on the jth production line, and the time C _ (ij (k +1)) for completing the (k +1) th process satisfies the following constraint:
Cij(k+1)＝STijk+Pij(k+1)
wherein, CiFor the completion time of the ith lot, CijkIndicates the end time, P, of the kth process step when the ith lot is produced on the jth production lineijkIndicates the processing time of the kth process in the production of the ith lot on the jth production line, STijkIndicating the start time of the kth process when the ith batch is produced on the jth production line.
5. The genetic algorithm-based production scheduling plan optimization method of claim 4, wherein: the machine constraints include the following:
(1) a factory machine number configuration table;
(2) a production line configuration table;
(3) the manufacturing rate of each procedure-equipment operation rate-equipment operation time table;
(4) a machine production parameter configuration table;
(5) dry-wet weight ratio of the product;
(6) each process switches the product-required schedule.
6. The genetic algorithm-based production scheduling plan optimization method of claim 1, wherein:
in step S3, the objective function of the model is as follows:
wherein F is the minimum maximum completion time, m is the number of batches, i means the ith batch, CiIs the completion time of the ith lot.
7. The genetic algorithm-based production scheduling plan optimization method of claim 1, wherein: in step S4, the specific genetic algorithm is implemented as follows:
s41: determining input and output variables and an encoding mode, wherein the input variables are as follows: order information; output variables are: an optimal production scheduling scheme; and (3) an encoding mode: splitting the order into a plurality of batches, and fully arranging the batch numbers;
s42: constructing a fitness function GeneticAlgorithm, namely sequentially calculating the production time of production for individuals formed in batches according to an algorithm;
s43: randomly generating an initial population farm and calculating a fitness function, wherein each individual is a production scheduling scheme in the population farm;
s44: sequencing the individuals according to a fitness function, and reserving the optimal Parent solution, namely reserving the best Parent production scheduling scheme;
s45: performing cross processing, namely selecting two parent schemes to be crossed from a plurality of randomly generated production scheduling schemes, wherein the specific cross operation is to exchange the batch production sequence in the two scheduling schemes and then generate two new child schemes;
s46: performing mutation treatment, namely generating a new production scheduling scheme by a random method to avoid the occurrence of local convergence;
s47: steps S44 through S46 are repeated until the fitness function converges, resulting in an optimal solution.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112947324A (en) * | 2021-01-27 | 2021-06-11 | 广东工业大学 | Textile production scheduling optimization method, system, storage medium and computer equipment |
| CN113657647A (en) * | 2021-07-16 | 2021-11-16 | 东华大学 | Order allocation method for textile and clothing industry internet |
| CN114524119A (en) * | 2020-11-22 | 2022-05-24 | 广州宁基智能系统有限公司 | Flexible customized leatheroid package production scheme |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106097055A (en) * | 2016-06-08 | 2016-11-09 | 沈阳工业大学 | Enterprise order processing method under personalized customization demand |
| CN107392515A (en) * | 2017-06-01 | 2017-11-24 | 经纬软信科技无锡有限公司 | Thickness connection induction system stock control and line optimization method based on genetic algorithm |
| CN107831745A (en) * | 2017-11-09 | 2018-03-23 | 西南交通大学 | A kind of flexible job shop inserts single action state method for optimizing scheduling |
| CN107832983A (en) * | 2017-12-18 | 2018-03-23 | 华中科技大学 | Casting smelting based on Dynamic Programming and genetic algorithm batch planning and scheduling method |
| KR102076225B1 (en) * | 2019-05-21 | 2020-02-11 | 국방과학연구소 | Method and apparatus for optimizing mission planning of UAV(Unmanned Aerial Vehicle) |
-
2020
- 2020-06-16 CN CN202010547306.4A patent/CN111665808A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106097055A (en) * | 2016-06-08 | 2016-11-09 | 沈阳工业大学 | Enterprise order processing method under personalized customization demand |
| CN107392515A (en) * | 2017-06-01 | 2017-11-24 | 经纬软信科技无锡有限公司 | Thickness connection induction system stock control and line optimization method based on genetic algorithm |
| CN107831745A (en) * | 2017-11-09 | 2018-03-23 | 西南交通大学 | A kind of flexible job shop inserts single action state method for optimizing scheduling |
| CN107832983A (en) * | 2017-12-18 | 2018-03-23 | 华中科技大学 | Casting smelting based on Dynamic Programming and genetic algorithm batch planning and scheduling method |
| KR102076225B1 (en) * | 2019-05-21 | 2020-02-11 | 국방과학연구소 | Method and apparatus for optimizing mission planning of UAV(Unmanned Aerial Vehicle) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114524119A (en) * | 2020-11-22 | 2022-05-24 | 广州宁基智能系统有限公司 | Flexible customized leatheroid package production scheme |
| CN114524119B (en) * | 2020-11-22 | 2023-09-19 | 广州宁基智能系统有限公司 | Flexible custom paper wrapper packaging production scheme |
| CN112947324A (en) * | 2021-01-27 | 2021-06-11 | 广东工业大学 | Textile production scheduling optimization method, system, storage medium and computer equipment |
| CN113657647A (en) * | 2021-07-16 | 2021-11-16 | 东华大学 | Order allocation method for textile and clothing industry internet |
| CN113657647B (en) * | 2021-07-16 | 2023-10-10 | 东华大学 | Order distribution method for textile and clothing industry Internet |
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